A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.

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Metaheuristics: From Design to Implementation

Evolutionary algorithms often have to solve optimization problems in the presence of a wide range of uncertainties. Generally, uncertainties in evolutionary computation can be divided into the following four categories. First, the fitness function is noisy. Second, the design variables and/or the environmental parameters may change after optimization, and the quality of the obtained optimal solution should be robust against environmental changes or deviations from the optimal point. Third, the fitness function is approximated, which means that the fitness function suffers from approximation errors. Fourth, the optimum of the problem to be solved changes over time and, thus, the optimizer should be able to track the optimum continuously. In all these cases, additional measures must be taken so that evolutionary algorithms are still able to work satisfactorily. This paper attempts to provide a comprehensive overview of the related work within a unified framework, which has been scattered in a variety of research areas. Existing approaches to addressing different uncertainties are presented and discussed, and the relationship between the different categories of uncertainties are investigated. Finally, topics for future research are suggested.

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Evolutionary optimization in uncertain environments-a survey

: This research organizes, presents, and analyzes contemporary Multiobjective Evolutionary Algorithm (MOEA) research and associated Multiobjective Optimization Problems (MOPs). Using a consistent MOEA terminology and notation, each cited MOEAs' key factors are presented in tabular form for ease of MOEA identification and selection. A detailed quantitative and qualitative MOEA analysis is presented, providing a basis for conclusions about various MOEA-related issues. The traditional notion of building blocks is extended to the MOP domain in an effort to develop more effective and efficient MOEAs. Additionally, the MOEA community's limited test suites contain various functions whose origins and rationale for use are often unknown. Thus, using general test suite guidelines appropriate MOEA test function suites are substantiated and generated. An experimental methodology incorporating a solution database and appropriate metrics is offered as a proposed evaluation framework allowing absolute comparisons of specific MOEA approaches. Taken together, this document's classifications, analyses, and new innovations present a complete, contemporary view of current MOEA "state of the art" and possible future research. Researchers with basic EA knowledge may also use part of it as a largely self-contained introduction to MOEAs.

Multiobjective evolutionary algorithms: classifications, analyses, and new innovations

Evolutionary algorithms (EAs) have received increasing interests both in the academy and industry. One main difficulty in applying EAs to real-world applications is that EAs usually need a large number of fitness evaluations before a satisfying result can be obtained. However, fitness evaluations are not always straightforward in many real-world applications. Either an explicit fitness function does not exist, or the evaluation of the fitness is computationally very expensive. In both cases, it is necessary to estimate the fitness function by constructing an approximate model. In this paper, a comprehensive survey of the research on fitness approximation in evolutionary computation is presented. Main issues like approximation levels, approximate model management schemes, model construction techniques are reviewed. To conclude, open questions and interesting issues in the field are discussed.

https://epubs.surrey.ac.uk/7610/2/SC2005.pdf

A comprehensive survey of fitness approximation in evolutionary computation

Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations: selection, crossover and mutation.

Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real-coded genetic algorithms. Different models of genetic operators and some mechanisms available for studying the behaviour of this type of genetic algorithms are revised and compared.

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Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis

This article presents both the theoretical basis and some experimental results on Hierarchical Genetic Algorithms (HGAs). HGAs are explained in details, along with the advantages conferred by their multi-layered hierarchical topology. This topology is an excellent compromise to the classical exploration/exploitation dilemma. Another feature is the introduction of multiple models for optimization problems, within the frame of an HGA. We show that with such an architecture, it is possible to use a mix of simple models that are very fast and more complex models (with slower solvers), and still achieve the same quality as that obtained with only complex models. The different concepts presented in this paper are then illustrated via experiments on a Computational Fluid Dynamics problem, namely a nozzle reconstruction. The overall results are that a Hierarchical Genetic Algorithm using multiple models can achieve the same quality of results as that of a classic GA using only a complex model, but up to three times faster.

A Hierarchical Genetic Algorithm Using Multiple Models for Optimization

Combining game theory and genetic algorithms with application to DDM-nozzle optimization problems

Fast Convergence Thanks to Diversity.

The paper presents the recent developments in Hierarchical Parallel Evolutionary Algorithms to speed up optimisation of aerodynamic shapes. One is the implementation of different models in different layers of a Parallel Genetic Algorithm. The other is Asynchronous Hierarchical Evolution Strategy. These methods are employed to reconstruct a one-dimensional transonic nozzle and a two-dimensional aerofoil shape. Considerable speed up is achieved as a result.

Advances in Hierarchical, Parallel Evolutionary Algorithms for Aerodynamic Shape Optimisation

This paper describes how new evolutionary tools such as Genetic Algorithms (GAs) can solve complex optimization problems in aerospace industry. With the increasing demand for high innovation rates for new and improved products there is a constant pressure to reduce the costs and time for marketing. Concurrent engineering and multidisciplinary design optimization on distributed parallel computers are emergent tools for the reduction of time cycle design which can drastically contribute to those industrial targets. Robustness of search algorithms is moving on top priority to extract globa) solution from rugged multi-dimensional landscapes. Based on Darwin's natural selection principle and digital DNA representation, the GAs approach is a new sharing Information Technology. The blending of stochastic properties of genetic operators with a deterministic fitness classification of individuals guarantees diversity for GAs and makes them able to escape local minima where traditional methods fail. Therefore, they produce a much larger optimization effort. Numerical results are presented for the global solution of complex optimization or control problems in the following areas: 1) CFD : localisation and shape design for high-lift maximization of complex multi body; 2) GEM: multi-objective optimization using Pareto or Nash game strategies for the RCS minimisation of scattered field by active aerodynamic reflectors ; 3) Parallel Computing : parallelized GAS applied to shape optimum design for the reduction of drag. Copyright © 1998 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. The above results illustrate the robustness and promising highly parallel properties of GAs for future concurrent aerospace technologies.

Mourad Sefrioui

Papers

A Hierarchical Genetic Algorithm Using Multiple Models for Optimization

Combining game theory and genetic algorithms with application to DDM-nozzle optimization problems

Fast Convergence Thanks to Diversity.

Advances in Hierarchical, Parallel Evolutionary Algorithms for Aerodynamic Shape Optimisation

Evolutionary computational methods for complex design in aerodynamics